Articles tagged with new-interfaces

1. Interface to human language

   One approach to making use of LLMs is to see it as an oracle: we can say "solve this problem for me", or at least "give me a choice of solutions". As all the answers to our problems can theoretically be generated, we can go ahead and retire all the human theorists and engineers. Science is solved.

   This approach assumes that creative problem solving is a matter of rearranging existing knowledge. Or, if more knowledge is needed, that knowledge acquisition is a process of mechanically recording the universe. Both are mistaken. We don't know the algorithms for creative problem solving or knowledge acquisition. While the "LLM as oracle" approach will certainly produce advancements in knowledge retrieval, it won't create new knowledge.

   Another approach, one that we're following at Deep Drama, is to use the LLM primarily as an interface for human language. The potential of "LLM as language API" is greater than it seems. There is the obvious path of building more powerful User Interfaces far beyond chatbots. But there is also the potential to expand the scope of our software, beyond solving transactional problems to encompass as-yet-untapped social frameworks. Our software has blind spots. Large areas of human knowledge and experience have been overlooked by engineers because of the messiness of their interfaces.

   As an example, I used Deep Drama's LLM-powered Source tool to generate this Drama Theoretic model of a random news article:

   	Don	Joe	Nip	Uni	dilemmas
   Donald Trump
   Block the deal immediately if winning the 2024 election	✓
   Joe Biden
   Express opposition to the merger	✓	✓		✓	Don T wrt Joe Uni T wrt Joe
   United Steelworkers labour union
   Oppose the merger		⨉	✓	✓	Joe P wrt Uni Nip T wrt Uni
   Nippon Steel
   Acquire US Steel in a non-hostile deal worth $14.1 billion	⨉	⨉	✓	⨉	Don P wrt Nip Joe P wrt Nip Uni P wrt Nip
   Introduce new technology and capital to US Steel		⨉	✓	⨉	Joe P wrt Nip Uni P wrt Nip

   The model's format is explainable and programmable. By using the LLM as an interface to language, Deep Drama can then use this model as the basis for further analysis and interaction, both with LLMs and with traditional interfaces. Deep Drama keeps the knowledge, and the opportunity for creativity, in the hands of the human users.

2. Dramatic interfaces

   In their working lives, most people don’t make decisions in complex situations of conflict and cooperation. Most companies instinctively, or intentionally, steer away from situations of dramatic conflict. Business concerns itself with win-win situations: I sell, you buy.

   Many roles do confront dramatic conflict. If you work in government, the military, healthcare, education, policing, then conflict is inherent. You are used to making decisions with unreliable information, with questionable actors and uncertain outcomes.

   Could the corporate world do more in this area — is it possible to have a dramatic relationship with your customers, where risk, deception and cooperation are all possible outcomes? I believe the answer is yes — drama is necessary if we want to take advantage of LLM tech.

   Software interfaces are moving beyond the transactional - embodied in the push-button GUIs of every app which is itself a direct inheritance from the first mechanical machines.

   LLM tech gives us a possibility of creating new interfaces that accept, and work within, the dramatic possibilities of language. We can build software that forms goals, reasons, arguments. It can be in cooperation or conflict with its users. Thinking about software in these terms is essential as we start to build LLM-powered “agents”. Dramatic intelligence is needed where we don’t expect to program every step of the software’s lifetime. Finally, as no incumbent company will allow themselves this kind of relationship with their customer, it presents a huge opportunity for a new kind of LLM-native enterprise.

3. SEO at the end of days

   Google’s announced a GPT-4 killer, Gemini, for release by December. There’s no question that OpenAI have a massive lead, but from the outside, it doesn’t look unassailable. LLMs may have presented Google with the first real threat to their search business.

   LLMs hint at a new interface to computing. Microsoft’s strength isn’t interfaces, but sales. They can add LLM features to their software as customers need them. Apple can fall back on their hardware — and may see an increasing demand in consumer compute even as they slowly come around to updating iOS. But Google is only an interface. A single UI paradigm — search. They’ll need to move fast.

   I was talking SEO today, and the paradigm already feels outdated. People out there still discovering software through search engines like boomers with cable subscriptions.

4. LLMs are complementary

   LLMs (and generative AI generally) have an important feature: broad adoption of LLMs doesn’t require a change in consumer behaviour. LLMs are a complementary technology. We can deploy them right now to the cloud, to people’s smartphones and into enterprise software stacks.

   The iPhone’s success fostered a narrative: that swift widespread change of consumer behaviour would be inevitable if the promise was there. But in reality, the unprecedented rise of mobile was overdetermined: the hardware was finally good enough (after decades of development) at the same time that the internet was finally changing consumer behaviour (after many false starts).

   Crypto and VR proponents have been selling them as a wave of technology about to break over our heads. But adoption in their current forms requires consumers to adopt new behaviours — buying (and using) costly barely-good-enough headsets, or changing their financial arrangements. I don’t doubt that we’ll see these technologies changing people’s lives in the future, but it won’t happen until the transition becomes easier for consumers to swallow. Making Crypto and VR not worth betting your balance sheet on.

   LLMs are different. LLM-powered services can sit alongside all existing software stacks, seamlessly providing functionality that wasn’t possible before. Consumers continue using their cloud-backed web and mobile apps and as far as the customer is concerned, their software got better without them having to do anything.

   In this way, LLMs are more like the move to cloud. Cloud-powered features first showed up as options within our traditional desktop apps. “Share” or “Save to Cloud” buttons (with the floppy disk icon, naturally). Users didn’t need to know what it all meant, they could just opt-in to the new functionality alongside their existing workflows. Eventually, consumer’s came to intuit the new model, and apps changed, but it was a gradual process.

   Each technology has its own path to adoption. Growth of LLM-powered services will be smoother than cloud, as it doesn’t need big-bang digital transformations. It’s possible to re-write individual functions within an existing system to take advantage of LLMs. The tech is complementary to our current cloud, web and mobile platforms.